Maneuver Decision-Making of Deep Learning for UCAV Thorough Azimuth Angles

Abstract

Maneuver decision-making directly determines the success or failure of air combat. To improve the dogfight ability of unmanned combat aerial vehicles and avoid the deficiencies of traditional methods, such as poor flexibility and a weak decision-making ability, a maneuver method using deep learning is proposed. A total of 72 different maneuvers are constructed, and 544320 states are designed. Flight simulations are conducted under these different states to obtain corresponding future azimuth angles. A deep neural network is trained with these offline data, and thus, the network possesses state prediction capability. A situation assessment function and a decision objective function based on azimuth angles are constructed. During air combat, the optimal maneuver is selected from the maneuver library according to the predicted state and the decision objective function. The results of air combat simulations indicate that the unmanned combat aerial vehicle (UCAV) can win the air combat game by the proposed method in a balanced situation and can meet missile launching conditions in an adverse situation. The operational time of this method has been reduced by 0.01 s compared with the comparison method.

Keywords

• Unmanned combat aerial vehicle
• decision-making
• deep learning
• air combat simulation
• situation assessment